impute: Impute missing values from a fitted MOFA model
In bioFAM/MOFA: Multi-Omics Factor Analysis (MOFA)

Description Usage Arguments Details Value Examples

This function uses the latent factors and the loadings inferred in order to impute missing values.

1 2	impute(object, views = "all", factors = "all", type = c("inRange", "response", "link"))

`object`	a `MOFAmodel` object.
`views`	character vector with the view names, or numeric vector with view indexes.
`factors`	character vector with the factor names, or numeric vector with the factor indexes.
`type`	type of prediction returned, either: response: gives the response vector, the mean for Gaussian and Poisson, and success probabilities for Bernoulli. link: gives the linear predictions. inRange: rounds the fitted values of integer-valued distributions (Poisson and Bernoulli) to the next integer. This is the default option.

Matrix factorization models generate a denoised and condensed low-dimensional representation of the data which capture the main sources of heterogeneity of the data. Such representation can be used to do predictions via the equation Y = WZ.
This method fills the ImputedData slot by replacing the missing values in the training data with the model predictions.
For more details see the Methods section of the MOFA article.

a MOFAmodel object with imputed data in the ImputedData slot

# Load CLL data
filepath <- system.file("extdata", "CLL_model.hdf5", package = "MOFAdata")
MOFA_CLL <- loadModel(filepath)
# impute missing data in all views using all factors
MOFA_CLL <- impute(MOFA_CLL)

# Load scMT data
filepath <- system.file("extdata", "scMT_model.hdf5", package = "MOFAdata")
MOFA_scMT <- loadModel(filepath)
# impute missing data in the RNA view using Factor 1
MOFA_scMT <- impute(MOFA_scMT, views="RNA expression", factors="LF1")